Probabilistic Threshold Research Articles

Accident avoidance and prediction system using adaptive probabilistic threshold monitoring technique

Identifying abnormal occasions in an observed area has been of the significant applications in Wireless Sensor Networks (WSNs) and the Internet of Things. Accidents and property harm can be maintained a strategic distance from if precise alarms are informed on time. In conventional checking techniques, a predefined threshold is given, and a signal is activated when the sensor perusing surpasses this threshold. This Single Threshold based Monitoring (STM) experiences the substandard nature of detected data, bringing about numerous false alarms. This work proposes an Adaptive Probabilistic Threshold Monitoring (APTM) technique for WSNs, where a signal is activated if the likelihood of the checked esteem being more significant than a predefined threshold (α) is more impressive than time delay (τ). The tight upper limits of the likelihood that controlled sum is more significant than the predetermined threshold are given. As indicated by the breaking points, probabilistic threshold-based algorithms for conglomeration checking are proposed. Broad execution assessment shows the adequacy of the proposed algorithms—the proposed design centers on observing the driver's level of diversion by checking optical parameters, health condition, driving example of the driver. It additionally screens street and activity conditions, the sudden entry of animal on the roadways. By a broad experimental assessment utilizing original dataset, the proposed algorithms beat the STM strategy in term of false alarm rate MSE is 2db.

Influence spread in two-layer interdependent networks: designed single-layer or random two-layer initial spreaders?

Influence spread in multi-layer interdependent networks (M-IDN) has been studied in the last few years; however, prior works mostly focused on the spread that is initiated in a single layer of an M-IDN. In real world scenarios, influence spread can happen concurrently among many or all components making up the topology of an M-IDN. This paper investigates the effectiveness of different influence spread strategies in M-IDNs by providing a comprehensive analysis of the time evolution of influence propagation given different initial spreader strategies. For this study we consider a two-layer interdependent network and a general probabilistic threshold influence spread model to evaluate the evolution of influence spread over time. For a given coupling scenario, we tested multiple interdependent topologies, composed of layers A and B, against four cases of initial spreader selection: (1) random initial spreaders in A, (2) random initial spreaders in both A and B, (3) targeted initial spreaders using degree centrality in A, and (4) targeted initial spreaders using degree centrality in both A and B. Our results indicate that the effectiveness of influence spread highly depends on network topologies, the way they are coupled, and our knowledge of the network structure — thus an initial spread starting in only A can be as effective as initial spread starting in both A and B concurrently. Similarly, random initial spread in multiple layers of an interdependent system can be more severe than a comparable initial spread in a single layer. Our results can be easily extended to different types of event propagation in multi-layer interdependent networks such as information/misinformation propagation in online social networks, disease propagation in offline social networks, and failure/attack propagation in cyber-physical systems.

Probabilistic thresholds for landslides warning by integrating soil moisture conditions with rainfall thresholds

Various methods have been proposed to define the rainfall thresholds for the landslide prediction. Once the threshold is determined, it remains the same regardless of the antecedent soil moisture conditions. However, given the important role of the antecedent soil moisture in the initiation of landslides, it is considered if the rainfall threshold level varies according to the antecedent soil moisture conditions, the prediction performance will be improved. Therefore, in this study we propose a probabilistic threshold to integrate antecedent soil moisture conditions with rainfall thresholds. In order to take into account the conditions with landslides and without landslides, the Bayesian analysis is applied to estimate the landslide occurrence probability given the various combinations of two factors: the antecedent soil moisture and the severity of the recent rainfall event. These combinations are then divided into conditions that are likely to trigger landslides and those unlikely to trigger landslides by comparing their probabilities with a critical value. In this way, the probabilistic threshold is determined. Here the soil moisture is estimated using the distributed hydrological model, and the severity of the rainfall event is characterized by the cumulated event rainfall-rainfall duration (ED) thresholds with different exceedance probabilities. The proposed approach was applied to a sub-region of the Emilia-Romagna region in northern Italy. The results show that the probabilistic threshold has a better prediction performance than the ED rainfall threshold, especially in terms of reducing false alarms. This study provides an effective approach to improve the prediction capability of the ED rainfall threshold, benefiting its application in the landslide prediction.

A Probabilistic Model to Support Radiologists’ Classification Decisions in Mammography Practice

We developed a probabilistic model to support the classification decisions made by radiologists in mammography practice. Using the feature observations and Breast Imaging Reporting and Data System (BI-RADS) classifications from radiologists examining diagnostic and screening mammograms, we modeled their decisions to understand their judgments. Our model could help improve the decisions made by radiologists using their own feature observations and classifications while maintaining their observed sensitivities. Based on 112,433 mammographic cases from 36,111 patients and 13 radiologists at 2 separate institutions with a 1.1% prevalence of malignancy, we trained a probabilistic Bayesian network (BN) to estimate the malignancy probabilities of lesions. For each radiologist, we learned an observed probabilistic threshold within the model. We compared the sensitivity and specificity of each radiologist against the BN model using either their observed threshold or the standard 2% threshold recommended by BI-RADS. We found significant variability among the radiologists' observed thresholds. By applying the observed thresholds, the BN model showed a 0.01% (1 case) increase in false negatives and a 28.9% (3612 cases) reduction in false positives. When using the standard 2% BI-RADS-recommended threshold, there was a 26.7% (47 cases) increase in false negatives and a 47.3% (5911 cases) reduction in false positives. Our results show that we can significantly reduce screening mammography false positives with a minimal increase in false negatives. We find that learning radiologists' observed thresholds provides valuable information regarding the conservativeness of clinical practice and allows us to quantify the variability in sensitivity across and within institutions. Our model could provide support to radiologists to improve their performance and consistency within mammography practice.

Pitch Class and Envelope Effects in the Tritone Paradox Are Mediated by Differently Pronounced Frequency Preference Regions.

Shepard tones (octave complex tones) are well defined in pitch chroma but are ambiguous in pitch height. Pitch direction judgments of Shepard tones depend on the clockwise distance of the pitch classes on the pitch class circle, indicating the proximity principle in auditory perception. The tritone paradox emerges when two Shepard tones that form a tritone interval are presented successively. In this case, no proximity cue is available and judgments depend on the first tone and vary from person to person. A common explanation for the tritone paradox is the assumption of a specific pitch class comparison mechanism based on a pitch class template that is differently orientated from person to person. In contrast, psychoacoustic approaches (e.g., the Terhardt virtual pitch theory) explain it with common pitch-processing mechanisms. The present paper proposes a probabilistic threshold model, which estimates Shepard tone pitch height by a probabilistic fundamental frequency extraction. In the first processing stage, only those frequency components whose amplitudes are above specific randomly distributed threshold values are selected for further processing, and whose expected values are determined by a threshold function. The lowest of these nonfiltered components is dedicated to the pitch height. The model is designed for tone pairs and provides occurrence probabilities for descending judgments. In a pitch-matching pretest, 12 Shepard tones (generated under a cosine envelope centered at 261 Hz) were compared to pure tones, whose frequencies were adjusted by an up-down staircase method. Matched frequencies corresponded to frequency components but were ambiguous in octave position. In order to test the model, Shepard tones were generated under six cosine envelopes centered over a wide frequency range (65.41, 261, 370, 440, 523.25, 1244.51 Hz). The model predicted pitch class effects and envelope effects. Steep threshold functions caused pronounced pitch class, whereas flat threshold functions caused pronounced envelope effects. The model provides an alternative explanation to the pitch class template theory and serves as a psychoacoustic framework for the perception of Shepard tones.

Performance modelling of a multirate loss system with batched Poisson arrivals under a probabilistic threshold policy

The authors consider a link accommodating batched Poisson arriving calls of different service classes. A batch of generally distributed number of calls arrive to the link at exponentially distributed time-points. Each call is treated separately from the rest, and its acceptance is decided, according to the available link bandwidth (partial batch blocking discipline). For congestion control, if the number of in-service calls of a service class exceeds a threshold (dedicated to the service class), a new call of the same service class is accepted with a probability, dependent on the system state (probabilistic threshold policy). The link is analysed as a multirate loss system, via a reversible Markov chain. The latter leads to a product form solution (PFS) for the steady-state distribution. Based on the PFS, the authors propose models for the accurate determination of time and call congestion probabilities and link utilisation. Comparison against other existing models under the complete sharing or the bandwidth reservation policy reveals the necessity and consistency of the proposed models.

English

A mobile sensor network is a set of physically distributed sensor nodes. Sensor node is a small wireless device with limited battery life, radio transmission range and storage size. Sensor nodes perform the task of collecting data, processing of data, monitoring of environment etc. Mobility of sensors can be effectively used to improve the target coverage quality and network connectivity in randomly deployed mobile sensor networks. Appropriate cluster head (CH) selection can reduce the energy consumption. Most famous hierarchical routing protocol is Low energy adaptive clustering hierarchical (LEACH), in which CH is selected on rotation basis based on a probabilistic threshold value. In this process only CH are allowed to send information to base stations (BS).But in this method a SCH i.e. super cluster head is chosen among the CHs who can send the information to mobile base stations by choosing suitable fuzzy descriptors like base station mobility, clusters centrality and remaining battery power. Fuzzy inference engine is used to select the chance of CHs to be SCH. We have derived the results from NS-2 simulator and show that the proposed protocol performs better than the LEACH protocol as it provides better stability and better lifetime to network. Keywords —Wireless Sensor Network (WSN), Super Cluster Head (SCH), Fuzzy Logic

A geomorphic approach to 100-year floodplain mapping for the Conterminous United States

Floodplain mapping using hydrodynamic models is difficult in data scarce regions. Additionally, using hydrodynamic models to map floodplain over large stream network can be computationally challenging. Some of these limitations of floodplain mapping using hydrodynamic modeling can be overcome by developing computationally efficient statistical methods to identify floodplains in large and ungauged watersheds using publicly available data. This paper proposes a geomorphic model to generate probabilistic 100-year floodplain maps for the Conterminous United States (CONUS). The proposed model first categorizes the watersheds in the CONUS into three classes based on the height of the water surface corresponding to the 100-year flood from the streambed. Next, the probability that any watershed in the CONUS belongs to one of these three classes is computed through supervised classification using watershed characteristics related to topography, hydrography, land use and climate. The result of this classification is then fed into a probabilistic threshold binary classifier (PTBC) to generate the probabilistic 100-year floodplain maps. The supervised classification algorithm is trained by using the 100-year Flood Insurance Rated Maps (FIRM) from the U.S. Federal Emergency Management Agency (FEMA). FEMA FIRMs are also used to validate the performance of the proposed model in areas not included in the training. Additionally, HEC-RAS model generated flood inundation extents are used to validate the model performance at fifteen sites that lack FEMA maps. Validation results show that the probabilistic 100-year floodplain maps, generated by proposed model, match well with both FEMA and HEC-RAS generated maps. On average, the error of predicted flood extents is around 14% across the CONUS. The high accuracy of the validation results shows the reliability of the geomorphic model as an alternative approach for fast and cost effective delineation of 100-year floodplains for the CONUS.

Evaluating the cost-effectiveness of hydrogel spacer in radiotherapy (RT) of prostate cancer (PC).

43 Background: A hydrogel rectal spacer (HRS) is an FDA-approved medical device used to increase the separation between the rectum and the prostate. A recent phase III trial demonstrated a small reduction in the incidence of RT toxicities associated with use of HRS. We conducted a cost-effectiveness analysis of HRS use in PC patients undergoing intensity modulated radiation therapy (IMRT). Methods: A multi-state Markov model was constructed to examine the cost-effectiveness of HRS in men with localized PC receiving IMRT in the US (arms: IMRT alone vs. IMRT + HRS). Subgroups included delivery site of IMRT (hospital vs. ambulatory) and baseline sexual function (SF) (general population vs. those with good SF). Based on previous studies, recurrence and survival were assumed equal for both arms. Data on SF, gastrointestinal and genitourinary toxicities incidence, as well as potential risks associated with HRS implantation were obtained from a recently published clinical trial. Health utilities and costs were derived from the literature and 2018 Physician Fee Schedule. Quality-adjusted life years (QALYs) and costs were modeled for a 5-year period from receipt of RT. Probabilistic sensitivity analysis (PSA) and value-based threshold analysis were conducted. Costs and utilities were discounted at 3% annually. Results: The per-person 5-year incremental cost for HRS administered in a hospital was $4,008 and the incremental effectiveness was 0.0273 QALYs. The incremental cost-effectiveness ratio (ICER) was $146,746 (95% credible interval from PSA $125,638 – $178,049) for PC patients undergoing HRS insertion in a hospital vs. $73,359 ($66,732 – $86,767) for patients undergoing HRS insertion in an ambulatory facility. For men with good SF, the ICER was $55,153 ($46,002 – $76,090) and $26,542 ($17,399 – $46,044) in hospital vs. ambulatory facility. Conclusions: This study is the first to evaluate the cost-effectiveness of HRS based on long-term toxicity data. Based on the current Medicare Physician Fee Schedule, HRS is cost-effective in men with good SF at a willingness to pay threshold of $100,000 and it is marginally cost-effective for the entire population depending on the facility where the HRS is inserted.

State-Dependent Bandwidth Sharing Policies for Wireless Multirate Loss Networks

We consider a reference cell of fixed capacity in a wireless cellular network while concentrating on next-generation network architectures. The cell accommodates new and handover calls from different service-classes. Arriving calls follow a random or quasi-random process and compete for service in the cell under two bandwidth sharing policies: 1) a probabilistic threshold (PrTH) policy or 2) the multiple fractional channel reservation (MFCR) policy. In the PrTH policy, if the number of in-service calls (new or handover) of a service-class exceeds a threshold (difference between new and handover calls), then an arriving call of the same service-class is accepted in the cell with a predefined state-dependent probability. In the MFCR policy, a real number of channels is reserved to benefit calls of certain service-classes; thus, a service priority is introduced. The cell is modeled as a multirate loss system. Under the PrTH policy, call-level performance measures are determined via accurate convolution algorithms, while under the MFCR policy, via approximate but efficient models. Furthermore, we discuss the applicability of the proposed models in 4G/5G networks. The accuracy of the proposed models is verified through simulation. Comparison against other models reveals the necessity of the new models and policies.

A big data driven sustainable manufacturing framework for condition-based maintenance prediction

Smart manufacturing refers to a future-state of manufacturing and it can lead to remarkable changes in all aspects of operations through minimizing energy and material usage while simultaneously maximizing sustainability enabling a futuristic more digitalized scenario of manufacturing. This research develops a big data analytics framework that optimizes the maintenance schedule through condition-based maintenance (CBM) optimization and also improves the prediction accuracy to quantify the remaining life prediction uncertainty. Through effective utilization of condition monitoring and prediction information, CBM would enhance equipment reliability leading to reduction in maintenance cost. The proposed framework uses a CBM optimization method that utilizes a new linguistic interval-valued fuzzy reasoning method for predicting the information. The proposed big data analytics framework in our study for estimating the uncertainty based on backward feature elimination and fuzzy unordered rule induction algorithm prediction errors, is an innovative contribution to the remaining life prediction field. Our paper elaborates on the basic underlying structure of CBM system that is defined by transaction matrix and the threshold value of failure probability. We developed this framework for analysing the CBM policy cost more accurately and to find the probabilistic threshold values of covariate that corresponds to the lowest price of predictive maintenance cost. The experimental results are performed on a big dataset which is generated from a sophisticated simulator of a gas turbine propulsion plant. A comparative analysis confirms that the method used in the proposed framework outpaces the classical methods in terms of classification accuracy and other statistical performance evaluation metrics.

Reporting l most influential objects in uncertain databases based on probabilistic reverse top-k queries

Reverse top-k queries are proposed from the perspective of a product manufacturer, which are essential for manufacturers to assess the potential market. However, the existing approaches for reverse top-k queries are all based on the assumption that the underlying data are exact (or certain). Due to the intrinsic differences between uncertain and certain data, these methods cannot be applied to process uncertain data sets directly. Motivated by this, in this paper, we firstly model the probabilistic reverse top-k queries over uncertain data. Moreover, we formulate a probabilistic top-l influential query, that reports the l most influential objects having the largest impact factors, where the impact factor of an object is defined as the cardinality of its probabilistic reverse top-k query result set. We present effective pruning heuristics for speeding up the queries. Particularly, we exploit several properties of probabilistic threshold top-k queries and probabilistic skyline queries to reduce the search space of this problem. In addition, an upper bound of the potential users is estimated to reduce the cost of computing the probabilistic reverse top-k queries for the candidate objects. Finally, efficient query algorithms are presented seamlessly with integration of the proposed pruning strategies. Extensive experiments using both real-world and synthetic data sets demonstrate the efficiency and effectiveness of our proposed algorithms.

Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach

Electrocardiogram (ECG) signals sensed from mobile devices pertain the potential for biometric identity recognition applicable in remote access control systems where enhanced data security is demanding. In this study, we propose a new algorithm that consists of a two-stage classifier combining random forest and wavelet distance measure through a probabilistic threshold schema, to improve the effectiveness and robustness of a biometric recognition system using ECG data acquired from a biosensor integrated into mobile devices. The proposed algorithm is evaluated using a mixed dataset from 184 subjects under different health conditions. The proposed two-stage classifier achieves a total of 99.52% subject verification accuracy, better than the 98.33% accuracy from random forest alone and 96.31% accuracy from wavelet distance measure algorithm alone. These results demonstrate the superiority of the proposed algorithm for biometric identification, hence supporting its practicality in areas such as cloud data security, cyber-security or remote healthcare systems.

A probabilistic threshold analysis of metformin (Met) with enzalutamide (Enza) to determine the cost and added efficacy needed to make such a combination theray cost-effective (CE).

e577 Background: Enza has become known for its' effectiveness in treating patients with metastatic castrate-resistant prostate cancer. However, Enza is expensive (~$3175/month) and unlikely to be CE at any willingness to pay (WTP) threshold from $0-$125 000/quality-adjusted life years (QALYs). Met is inexpensive (~$8.00/month) and recently two large population-based studies of prostate cancer (PCa) demonstrated that diabetics taking Met had improved PCa specific and overall survival compared to those not taking Met. As a result we theorized that there must be a cost of Enza and a specific added effect Met could provide that would make Met a CE adjuvant therapy to Enza even though it is not currently used as such. Methods: We constructed a Markov-based decision analytic model and performed a probabilistic threshold analysis to narrow in on several combinations of costs of Enza and added efficacies needed to make Enza + Met a CE combination therapy at different WTP thresholds. Costs, utilities, and transition probabilities were derived from existing literature. Effectiveness was measured using QALYs. Costs and QALYs were considered over a lifetime horizon and discounted at 5% per annum. Results: At a WTP threshold of $50 000/QALY Enza + Met is unlikely to be CE no matter the cost decrement or added efficacy applied to the model. At a WTP threshold of $75 000/QALY Met is also unlikely to be CE unless the price of Enza could drop to $1934/month and Met could also add 1% to the efficacy of Enza. At Enza's current cost and a WTP threshold of $100 000, Enza + Met could be CE barring Met adds 0.621% to the number of patients still on treatment at 36 months. Conclusions: Enza + Met is unlikely to be CE at WTP thresholds of $50 000/QALY or $75 000/QALY unless there is a significant price drop for Enza; these results make sense because a therapy that is considered not CE at these WTP thresholds by itself is unlikely to be CE with a theoretical adjuvant therapy that would hold a patient on such a treatment for even longer. Our results show that the only way for Enza + Met to be CE is through the theoretical overall survival benefit of Met as the price of Enza is unlikely to drop any time soon.

Framework for Managing Uncertain Distributed Categorical Data

In recent years, data has become uncertain due to the flourishing advanced technologies that participate continuously and increasingly in producing large amounts of incomplete data. Often, many modern applications where uncertainty occurs are distributed in nature, e.g., distributed sensor networks, information extraction, data integration, social network, etc. Consequently, even though the data uncertainty has been studied in the past for centralized behavior, it is still a challenging issue to manage uncertainty over the data in situ. In this paper, we propose a framework to managing uncertain categorical data over distributed environments that is built upon a hierarchical indexing technique based on inverted index, and a distributed algorithm to efficiently process queries on uncertain data in distributed environment. Leveraging this indexing technique, we address two kinds of queries on the distributed uncertain databases 1) a distributed probabilistic thresholds query, where its answers satisfy the probabilistic threshold requirement; and 2) a distributed top-k-queries, optimizing, the transfer of the tuples from the distributed sources to the coordinator site and the time treatment. Extensive experiments are conducted to verify the effectiveness and efficiency of the proposed method in terms of communication costs and response time.

Top Favorite Probabilistic Products Queries

With the development of the economy, products are significantly enriched, and uncertainty has been their inherent quality. The probabilistic dynamic skyline (PDS) query is a powerful tool for customers to use in selecting products according to their preferences. However, this query suffers several limitations: it requires the specification of a probabilistic threshold, which reports undesirable results and disregards important results; it only focuses on the objects that have large dynamic skyline probabilities; and, additionally, the results are not stable. To address this concern, in this paper, we formulate an uncertain dynamic skyline (UDS) query over a probabilistic product set. Furthermore, we propose effective pruning strategies for the UDS query, and integrate them into effective algorithms. In addition, a novel query type, namely the top <inline-formula><tex-math notation="LaTeX">$k$</tex-math> </inline-formula> favorite probabilistic products (TFPP) query, is presented. The TFPP query is utilized to select <inline-formula> <tex-math notation="LaTeX">$k$</tex-math></inline-formula> products which can meet the needs of a customer set at the maximum level. To tackle the TFPP query, we propose a TFPP algorithm and its efficient parallelization. Extensive experiments with a variety of experimental settings illustrate the efficiency and effectiveness of our proposed algorithms.

A Quantitative Framework for Defining “How Safe is Safe Enough?” in Crewed Spacecraft

Abstract This article presents a quantitative framework that can be used to determine whether a spacecraft is “Safe Enough” for crewed flight. Three major elements are established, namely: (1) An unequivocal definition of “Safe” (and its inverse, “Unsafe”), derived from and consistent with current NASA terminology and empirical practice, (2) a quantitative risk spectrum, which highlights spacecraft risk (measured using Probabilistic Risk Assessment) against a reference standard, and (3) a probabilistic threshold value, which delineates risk that is acceptable (e.g., “Safe Enough”) with risk that is unacceptable (e.g., “Not Safe Enough”). Each element of the framework is developed concomitantly, step-wise, and from the bottom-up, to ensure “Safe Enough” can be reliably and systematically determined. “When we first started [flying in space], people would say things like, well the spacecraft's got to be ‘good’. But what the hell does ‘good’ mean?”1 —Glynn Lunney, Apollo Flight Director

Reduced complexity sphere decoding using probabilistic threshold based Schnorr–Euchner enumeration

The use of multiple antennas at both the transmitter and the receiver is the key technology for future wireless communications since it achieves higher spectral efficiency. Symbol decoding at the receiver for such a system is still a challenge. We propose a new enumeration technique, termed as probabilistic threshold-based Schnorr–Euchner (PSE) enumeration, to reduce the complexity of sphere decoding (SD). The conventional SE-SD algorithm visits all nodes satisfying the sphere constraint in ascending order of their branch metric values. However, this algorithm is computationally expensive in real-time scenarios. Therefore, we use a threshold limit for the branch metric values in the Schnorr–Euchner (SE) enumeration which is based on the noise statistics, to reduce the overall complexity. In each layer of the decoding process, the proposed PSE-SD technique performs a threshold test before visiting the next candidate in an SE ordered list. As a result, unlikely nodes, which do not satisfy the threshold test, are pruned earlier than in the conventional SE-SD algorithm. From the simulation results, we examine the influence of the proposed probabilistic threshold for various MIMO configurations and witness that the PSE-SD technique achieves a significant complexity reduction over existing methods with only a negligible performance loss.

A Fuzzy Logic-Based Clustering Algorithm for WSN to Extend the Network Lifetime

Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited computation, communication, memory, and energy resources that are being used for huge range of applications where the traditional infrastructure-based network is mostly infeasible. The sensor nodes are densely deployed in a hostile environment to monitor, detect, and analyze the physical phenomenon and consume considerable amount of energy while transmitting the information. It is impractical and sometimes impossible to replace the battery and to maintain longer network life time. So, there is a limitation on the lifetime of the battery power and energy conservation is a challenging issue. Appropriate cluster head (CH) election is one such issue, which can reduce the energy consumption dramatically. Low energy adaptive clustering hierarchy (LEACH) is the most famous hierarchical routing protocol, where the CH is elected in rotation basis based on a probabilistic threshold value and only CHs are allowed to send the information to the base station (BS). But in this approach, a super-CH (SCH) is elected among the CHs who can only send the information to the mobile BS by choosing suitable fuzzy descriptors, such as remaining battery power, mobility of BS, and centrality of the clusters. Fuzzy inference engine (Mamdani's rule) is used to elect the chance to be the SCH. The results have been derived from NS-2 simulator and show that the proposed protocol performs better than the LEACH protocol in terms of the first node dies, half node alive, better stability, and better lifetime.

Uncertain top-k query processing in distributed environments

The top-k query on uncertain data set has been a very hot topic these years, and there have been many studies on uncertain top-k queries. Unfortunately, most of the existing algorithms only consider centralized processing environments, and they are not suitable for the large-scale data. In this paper, it is the first attempt to process probabilistic threshold top-k queries (an important uncertain top-k query, PT-k for short) in a distributed environment. We propose 3 efficient algorithms. The serial distributed approach adopts a new method, which only requires a few amount of calculations, to serially process PT-k queries in distributed environments. The global sorting first algorithm for PT-k query processing (GSP) is designed for improving the computation speed. In GSP, a distributed sorting operation is performed, and then we compute the candidates for PT-k queries in parallel. The query results can be computed by using a novel incremental method which can reduce the number of calculations. The local filtering first algorithm for PT-k query processing is designed for reducing the network overhead. Specifically, several filtering strategies are proposed to filter out redundant data locally, and then the incremental method in GSP is used to process the PT-k queries. Finally, the effectiveness of our proposed algorithms is verified through a series of experiments.